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Multivariate linear regression

returns
the estimated coefficients for a multivariate normal regression of
the `beta`

= mvregress(`X`

,`Y`

)*d*-dimensional responses in `Y`

on
the design matrices in `X`

.

returns
the estimated coefficients using additional options specified by one
or more name-value pair arguments. For example, you can specify the
estimation algorithm, initial estimate values, or maximum number of
iterations for the regression.`beta`

= mvregress(`X`

,`Y`

,`Name,Value`

)

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2nd ed., Hoboken, NJ: John Wiley & Sons, Inc., 2002.

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[3] Sexton, Joe, and A. R. Swensen. “ECM
Algorithms that Converge at the Rate of EM.” *Biometrika*.
Vol. 87, No. 3, 2000, pp. 651–662.

[4] Dempster, A. P., N. M. Laird, and D. B.
Rubin. “Maximum Likelihood from Incomplete Data via the EM
Algorithm.” *Journal of the Royal Statistical Society*.
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